How Do Recommendation Models Amplify Popularity Bias? An Analysis from the Spectral Perspective

YuanqingYu, ChongmingGao, Jiawei Chen, Heng Tang, YuefengSun, QianChen, WeizhiMa, MinZhang
January 2024
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Abstract

Reinforcement Learning (RL)-Based Recommender Systems (RSs) have gained rising attention for their potential to enhance long-term user engagement. However, research in this field faces challenges, including the lack of user-friendly frameworks, inconsistent evaluation metrics, and difficulties in reproducing existing studies. To tackle these issues, we introduce EasyRL4Rec, an easy-to-use code library designed specifically for RL-based RSs. This library provides lightweight and diverse RL environments based on five public datasets and includes core modules with rich options, simplifying model development. It provides unified evaluation standards focusing on long-term outcomes and offers tailored designs for state modeling and action representation for recommendation scenarios. Furthermore, we share our findings from insightful experiments with current methods. EasyRL4Rec seeks to facilitate the model development and experimental process in the domain of RL-based RSs. The library is available for public use.

Type
Recommendation-Systems
Publication
In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information
Recommender Systems Reinforcement Learning Code Library
Jiawei Chen
Jiawei Chen
陈佳伟 研究员
Heng Tang
Heng Tang
Student

Heng Tang is currently a Master student in ZLST, where he is supervised by Prof. Can Wang and Prof. Jiawei Chen.